Ensuring equitable access to healthcare in the age of algorithms and AI

Yesterday, Dr. Peter Vaughan, chair of the board of directors of Canada Health Infoway, spoke at Longwoods’ Breakfast with the Chiefs.

After outlining the current state and future perspectives of digitization in healthcare, his main message was two-fold: 1. We are at risk of a “failure of imagination”, i.e. we cannot fathom all the possible futures that digital disruption might confront us with and hence fail to plan for their pitfalls adequately. 2. There is great potential for algorithms to be built in such a way as to solidify and deepen inequalities that currently exist in our system, and we need government oversight of such algorithms to prevent this from happening.

The first point is easy to understand, the second point may need little more explanation. Algorithms are used widely to determine what information is presented to us online, what choices are offered to us. We are all familiar with websites, offering us items we ‘might also like’, based on our past choices and based on what other purchasers have bought.

At a time when data from various sources can be linked to create sophisticated profiles of people, it would be easy for a healthcare organization to identify individuals that are potentially ‘high cost’ and to deny them service or to restrict access to services. Bias can creep into algorithms quickly. If people of a certain age, ethnic background or location are deemed to be ‘higher risk’ for some health issues or for unhealthy behaviours, and this is built into an algorithm that prioritizes ‘lower risk’ customers, then you are discriminated against if you share the same profile, no matter how you actually behave.

Discrimination is often systemic, unless a conscious effort is made to break the cycle of disadvantaged circumstances leading to failure to thrive leading to lower opportunity in the future. As Dr. Peter Vaughan pointed out, we in Canada value equitable access to healthcare, education and other public goods. We expect our government to put safeguards in place against discrimination based on background and circumstances. But how can this be done?

Private, for-profit enterprises have a right to segment their customers and offer different services to different tiers, based on their profitability or ‘life-time customer value’. Companies do this all the time, it is good business practice. But what about a private digital health service that accepts people with low risk profiles into their patient roster, but is unavailable to others, whose profile suggests they may need a lot of services down the line? Is this acceptable?

And if the government were to monitor and regulate algorithms related to the provision of public goods (such as healthcare) who has the right credentials to tackle this issue? People would be needed who understand data science – how algorithms are constructed and how AI feeds into them – and social sciences – to identify the assumptions underpinning the algorithms – and ethics. Since technology is moving very fast, we should have started training such people yesterday.

And how could algorithms be tested? Should this be part of some sort of an approval process? Can testing be done by individuals, relying on their expertise and judgement? Or could there be a more controlled way of assessing algorithms for their potential to disadvantage certain members of society? Or a potential for automation of this process?

I am thinking there may be an opportunity here to develop a standardized set of testing tools that algorithms could be subjected to. For example, one could create profiles that represent different groups in society and test-run them as fake applicants for this or that service.

Also, algorithms change all the time, so one would perhaps need to have a process of re-certification in place to ensure continued compliance with the rules.

And then, there would be the temptation for companies to game the system. So, if a standardized set of test cases were developed to test algorithms for social acceptability, companies may develop code to identify and ‘appease’ these test cases but continue discriminating against real applicants.

In any case, this could be an interesting and important new field for social scientists to go into. However, one must be willing to combines the ‘soft’ social sciences with ‘hard’ stats and IT skills and find the right learning venues to develop these skills.

Much food for thought. Thank you, Dr. Peter Vaughan!

Old World, New World

This is not about market research. When I woke up last night I had a vivid memory of standing outside a door in an apartment building in Germany. There was the door, thickly painted wood, and the doorbell that I was about to ring. The stone floor cold under my feet, grayish-white speckled, sort of like marble, but definitely much harder than marble. Quiet, cool air in the house, and faint noises from playing children in the courtyard. A few steps down, a landing with an old double window. The window sill about 50 centimetres wide, it had some potted plants of the durable, all-season nature.

So many times I have been to places like this, stood outside of apartment doors, slightly apprehensive. The setting evokes a range of associations. The building as a microcosm. People have lived together for many years. Someone lovingly waters those plants, and dusts them off every once in a while. The floor is kept spotless, and I am sure there is a schedule posted somewhere, that tells which party is responsible for cleaning which week.

Corridor German House

A place of comfort. A place of confinement. Long-standing relationships, set ways, ancient enemies. There probably is a lady on the third floor who bangs a broomstick against her ceiling every time the family above her is audible. The couple on the ground floor always gripes about people not cleaning off their shoes properly and trudging dirt through the house. When kids talk loudly on the steps, someone will stick their head out their door with a disapproving look.

 

Fast forward to Toronto, Canada. First of all, a lot of people here own their own home. And not just rich people. Many single-family dwellings are not more than ten, twenty years old. My house was built in the 1940ies and is considered ‘old’. Having your own house means a lot of things. It means making as much noise (inside) as you want. Children jumping down the stairs, jelling, turning your music up. There are no rules to follow (well, very few), no customs to adhere to. Wear what you want, talk however you want, cook whatever you want. You are free to strike new relationships, don’t have to follow ‘what is proper’. What is proper and acceptable is negotiated every single day as people of different cultural backgrounds mingle and co-exist. Make no assumptions about others – speak to them and see what they are all about.

Townhouse Canada

This place is new, feels new. The depth is lacking, the ties woven through centuries (unless you go into small towns and more traditional parts of the country). It is a country full of opportunities. You have a good idea, you can get things done, we can benefit from it, you’re in. Don’t worry if your email contains grammatical errors, if you speak with an accent. Here in Toronto, most people are from somewhere else.

Your house is a blank slate. Make of it what you want.

Disclaimer:

I realize that I am writing this from a particular vantage point (as one usually does!). In Canada, there are many people who do not have the same opportunities as they have been open to me. If you arrive without language skills (English / French), without family connections and without financial backing, getting a foothold and making use of opportunities can be tough. However, I argue that the opportunities here are still greater than if you were to arrive in Germany with the same skill set and resources.

Conclusion:

Germany and Canada, both sets of circumstances can breed great things. Born out of the freedom to dream large or out of the necessity to come up with creative solutions in confined circumstances. Good luck to you all!